ZEPPI: Proteome-scale sequence-based evaluation of protein-protein interaction models.

We introduce ZEPPI (Z-score Evaluation of Protein-Protein Interfaces), a framework to evaluate structural models of a complex based on sequence coevolution and conservation involving residues in protein-protein interfaces. The ZEPPI score is calculated by comparing metrics for an interface to those obtained from randomly chosen residues. Since contacting residues are defined by the structural model, this obviates the need to account for indirect interactions. Further, although ZEPPI relies on species-paired multiple sequence alignments, its focus on interfacial residues allows it to leverage quite shallow alignments. ZEPPI can be implemented on a proteome-wide scale and is applied here to millions of structural models of dimeric complexes in the Escherichia coli and human interactomes found in the PrePPI database. PrePPI's scoring function is based primarily on the evaluation of protein-protein interfaces, and ZEPPI adds a new feature to this analysis through the incorporation of evolutionary information. ZEPPI performance is evaluated through applications to experimentally determined complexes and to decoys from the CASP-CAPRI experiment. As we discuss, the standard CAPRI scores used to evaluate docking models are based on model quality and not on the ability to give yes/no answers as to whether two proteins interact. ZEPPI is able to detect weak signals from PPI models that the CAPRI scores define as incorrect and, similarly, to identify potential PPIs defined as low confidence by the current PrePPI scoring function. A number of examples that illustrate how the combination of PrePPI and ZEPPI can yield functional hypotheses are provided.

Integrating 3D structural information into systems biology.

Systems biology is a data-heavy field that focuses on systems-wide depictions of biological phenomena necessarily sacrificing a detailed characterization of individual components. As an example, genome-wide protein interaction networks are widely used in systems biology and continuously extended and refined as new sources of evidence become available. Despite the vast amount of information about individual protein structures and protein complexes that has accumulated in the past 50 years in the Protein Data Bank, the data, computational tools, and language of structural biology are not an integral part of systems biology. However, increasing effort has been devoted to this integration, and the related literature is reviewed here. Relationships between proteins that are detected via structural similarity offer a rich source of information not available from sequence similarity, and homology modeling can be used to leverage Protein Data Bank structures to produce 3D models for a significant fraction of many proteomes. A number of structure-informed genomic and cross-species (i.e., virus-host) interactomes will be described, and the unique information they provide will be illustrated with a number of examples. Tissue- and tumor-specific interactomes have also been developed through computational strategies that exploit patient information and through genetic interactions available from increasingly sensitive screens. Strategies to integrate structural information with these alternate data sources will be described. Finally, efforts to link protein structure space with chemical compound space offer novel sources of information in drug design, off-target identification, and the identification of targets for compounds found to be effective in phenotypic screens.

Safety and tolerability of Vitamin D3 5000 IU/day in epilepsy.

PURPOSE: Preclinical and early clinical research indicates that Vitamin D3 may reduce seizures in both animal models and open-label clinical trials. METHODS: This is an initial report of an ongoing pilot study of oral Vitamin D3 5000 IU/day in subjects with drug-resistant epilepsy. After Institutional Review Board (IRB) approval and informed consent, subjects with ;less than one focal onset or generalized tonic-clonic seizure per month were enrolled. Subjects entered a 4-week baseline, followed by a 12-week treatment period. Serum 25, OH Vitamin D3, Blood Urea Nitrogen (BUN), creatinine, and calcium levels were monitored at baseline and at 6 and 12 weeks. RESULTS: High-dose Vitamin D3 5000 IU/day was well tolerated. Serum 25, OH Vitamin D3 levels increased significantly at six and twelve weeks. Vitamin D insufficiency, defined as a 25, OH Vitamin D3 level of <20 ng/ml normalized in all subjects with insufficient vitamin D levels. Median seizure frequency declined from 5.18 seizures per month to 3.64 seizures per month at 6 weeks and to 4.2 seizures per month at 12 weeks. The median percent change in seizure frequency was -26.9% at six weeks, and -10.7% at 12 weeks (not significant, Wilcoxon Signed Rank Test, P > 0.34). CONCLUSIONS: High-dose oral Vitamin D3, 5000 IU/day was safe and well tolerated in subjects with epilepsy. Vitamin D levels increased significantly at 6 and 12 weeks but never exceeded potentially toxic levels, defined as >100 ng/ml. To reduce variability, we will now recruit subjects who only have three or more seizures per month.

A prospective long-term study of external trigeminal nerve stimulation for drug-resistant epilepsy.

BACKGROUND: External trigeminal nerve stimulation (eTNS) is an emerging noninvasive therapy for drug-resistant epilepsy (DRE). We report the long-term safety and efficacy of eTNS after completion of a phase II randomized controlled clinical trial for drug-resistant epilepsy. METHODS: This was a prospective open-label long-term study. Subjects who completed the phase II randomized controlled trial of eTNS for DRE were offered long-term follow-up for 1year. Subjects who were originally randomized to control settings were crossed over to effective device parameters (30s on, 30s off, pulse duration of 250s, frequency of 120Hz). Efficacy was assessed using last observation carried forward or parametric imputation methods for missing data points. Outcomes included change in median seizure frequency, RRATIO, and 50% responder rate. RESULTS: Thirty-five of 50 subjects from the acute double-blind randomized controlled study continued in the long-term study. External trigeminal nerve stimulation was well tolerated. No serious device-related adverse events occurred through 12months of long-term treatment. At six and twelve months, the median seizure frequency for the original treatment group decreased by -2.39 seizures per month at 6 months (-27.4%) and -3.03 seizures per month at 12 months (-34.8%), respectively, from the initial baseline (p<0.05, signed-rank test). The 50% responder rates at three, six, and twelve months were 36.8% for the treatment group and 30.6% for all subjects. CONCLUSION: The results provide long-term evidence that external trigeminal nerve stimulation is a safe and promising long-term treatment for drug-resistant epilepsy.

Network-based elucidation of colon cancer drug resistance mechanisms by phosphoproteomic time-series analysis.

Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. Leveraging progress in proteomic technologies and network-based methodologies, we introduce Virtual Enrichment-based Signaling Protein-activity Analysis (VESPA)-an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations-and use it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogating tumor-specific enzyme/substrate interactions accurately infers kinase and phosphatase activity, based on their substrate phosphorylation state, effectively accounting for signal crosstalk and sparse phosphoproteome coverage. The analysis elucidates time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring, experimentally confirmed by CRISPR knock-out assays, suggesting broad applicability to cancer and other diseases.

Protein kinase Cθ C2 domain is a phosphotyrosine binding module that plays a key role in its activation.

Protein kinase Cθ (PKCθ) is a novel PKC that plays a key role in T lymphocyte activation. To understand how PKCθ is regulated in T cells, we investigated the properties of its N-terminal C2 domain that functions as an autoinhibitory domain. Our measurements show that a Tyr(P)-containing peptide derived from CDCP1 binds the C2 domain of PKCθ with high affinity and activates the enzyme activity of the intact protein. The Tyr(P) peptide also binds the C2 domain of PKCδ tightly, but no enzyme activation was observed with PKCδ. Mutations of PKCθ-C2 residues involved in Tyr(P) binding abrogated the enzyme activation and association of PKCθ with Tyr-phosphorylated full-length CDCP1 and severely inhibited the T cell receptor/CD28-mediated activation of a PKCθ-dependent reporter gene in T cells. Collectively, these studies establish the C2 domain of PKCθ as a Tyr(P)-binding domain and suggest that the domain may play a major role in PKCθ activation via its Tyr(P) binding.

ZEPPI: proteome-scale sequence-based evaluation of protein-protein interaction models.

We introduce ZEPPI (Z-score Evaluation of Protein-Protein Interfaces), a framework to evaluate structural models of a complex based on sequence co-evolution and conservation involving residues in protein-protein interfaces. The ZEPPI score is calculated by comparing metrics for an interface to those obtained from randomly chosen residues. Since contacting residues are defined by the structural model, this obviates the need to account for indirect interactions. Further, although ZEPPI relies on species-paired multiple sequence alignments, its focus on interfacial residues allows it to leverage quite shallow alignments. ZEPPI performance is evaluated through applications to experimentally determined complexes and to decoys from the CASP-CAPRI experiment. ZEPPI can be implemented on a proteome-wide scale as evidenced by calculations on millions of structural models of dimeric complexes in the E. coli and human interactomes found in the PrePPI database. A number of examples that illustrate how these tools can yield novel functional hypotheses are provided.

PrePPI: A structure informed proteome-wide database of protein-protein interactions.

We present an updated version of the Predicting Protein-Protein Interactions (PrePPI) webserver which predicts PPIs on a proteome-wide scale. PrePPI combines structural and non-structural clues within a Bayesian framework to compute a likelihood ratio (LR) for essentially every possible pair of proteins in a proteome; the current database is for the human interactome. The structural modeling (SM) clue is derived from templatebased modeling and its application on a proteome-wide scale is enabled by a unique scoring function used to evaluate a putative complex. The updated version of PrePPI leverages AlphaFold structures that are parsed into individual domains. As has been demonstrated in earlier applications, PrePPI performs extremely well as measured by receiver operating characteristic curves derived from testing on E. coli and human protein-protein interaction (PPI) databases. A PrePPI database of ~1.3 million human PPIs can be queried with a webserver application that comprises multiple functionalities for examining query proteins, template complexes, 3D models for predicted complexes, and related features ( https://honiglab.c2b2.columbia.edu/PrePPI ). PrePPI is a state-of- the-art resource that offers an unprecedented structure-informed view of the human interactome.

PrePPI: A Structure Informed Proteome-wide Database of Protein-Protein Interactions.

We present an updated version of the Predicting Protein-Protein Interactions (PrePPI) webserver which predicts PPIs on a proteome-wide scale. PrePPI combines structural and non-structural evidence within a Bayesian framework to compute a likelihood ratio (LR) for essentially every possible pair of proteins in a proteome; the current database is for the human interactome. The structural modeling (SM) component is derived from template-based modeling and its application on a proteome-wide scale is enabled by a unique scoring function used to evaluate a putative complex. The updated version of PrePPI leverages AlphaFold structures that are parsed into individual domains. As has been demonstrated in earlier applications, PrePPI performs extremely well as measured by receiver operating characteristic curves derived from testing on E. coli and human protein-protein interaction (PPI) databases. A PrePPI database of ∼1.3 million human PPIs can be queried with a webserver application that comprises multiple functionalities for examining query proteins, template complexes, 3D models for predicted complexes, and related features (https://honiglab.c2b2.columbia.edu/PrePPI). PrePPI is a state-of-the-art resource that offers an unprecedented structure-informed view of the human interactome.

PrePCI: A structure- and chemical similarity-informed database of predicted protein compound interactions.

We describe the Predicting Protein-Compound Interactions (PrePCI) database which comprises over 5 billion predicted interactions between 6.8 million chemical compounds and 19,797 human proteins. PrePCI relies on a proteome-wide database of structural models based on both traditional modeling techniques and the AlphaFold Protein Structure Database. Sequence- and structural similarity-based metrics are established between template proteins, T, in the Protein Data Bank that bind compounds, C, and query proteins in the model database, Q. When the metrics exceed threshold values, it is assumed that C also binds to Q with a likelihood ratio (LR) derived from machine learning. If the relationship is based on structural similarity, the LR is based on a scoring function that measures the extent to which C is compatible with the binding site of Q as described in the LT-scanner algorithm. For every predicted complex derived in this way, chemical similarity based on the Tanimoto coefficient identifies other small molecules that may bind to Q. An overall LR for the binding of C to Q is obtained from Naive Bayesian statistics. The PrePCI database can be queried by entering a UniProt ID or gene name for a protein to obtain a list of compounds predicted to bind to it along with associated LRs. Alternatively, entering an identifier for the compound outputs a list of proteins it is predicted to bind. Specific applications of the database to lead discovery, elucidation of drug mechanism of action, and biological function annotation are described.

Network-based elucidation of colon cancer drug resistance by phosphoproteomic time-series analysis.

Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. By leveraging progress in proteomic technologies and network-based methodologies, over the past decade, we developed VESPA-an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations-and used it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogation of tumor-specific enzyme/substrate interactions accurately inferred kinase and phosphatase activity, based on their inferred substrate phosphorylation state, effectively accounting for signal cross-talk and sparse phosphoproteome coverage. The analysis elucidated time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring that was experimentally confirmed by CRISPRko assays, suggesting broad applicability to cancer and other diseases.

Genome-wide structural analysis reveals novel membrane binding properties of AP180 N-terminal homology (ANTH) domains.

An increasing number of cytosolic proteins are shown to interact with membrane lipids during diverse cellular processes, but computational prediction of these proteins and their membrane binding behaviors remains challenging. Here, we introduce a new combinatorial computation protocol for systematic and robust functional prediction of membrane-binding proteins through high throughput homology modeling and in-depth calculation of biophysical properties. The approach was applied to the genomic scale identification of the AP180 N-terminal homology (ANTH) domain, one of the modular lipid binding domains, and prediction of their membrane binding properties. Our analysis yielded comprehensive coverage of the ANTH domain family and allowed classification and functional annotation of proteins based on the differences in local structural and biophysical features. Our analysis also identified a group of plant ANTH domains with unique structural features that may confer novel functionalities. Experimental characterization of a representative member of this subfamily confirmed its unique membrane binding mechanism and unprecedented membrane deforming activity. Collectively, these studies suggest that our new computational approach can be applied to genome-wide functional prediction of other lipid binding domains.

A molecular switch required for retrovirus assembly participates in the hexagonal immature lattice.

In the Rous sarcoma virus (RSV) Gag protein, the 25 amino-acid residues of the p10 domain immediately upstream of the CA domain are essential for immature particle formation. We performed systematic mutagenesis on this region and found excellent correlation between the amino-acid side chains required for in vitro assembly and those that participate in the p10-CA dimer interface in a previously described crystal structure. We introduced exogenous cysteine residues that were predicted to form disulphide bonds across the dimer interface. Upon oxidation of immature particles, a disulphide-linked Gag hexamer was formed, implying that p10 participates in and stabilizes the immature Gag hexamer. This is the first example of a critical interaction between two different Gag domains. Molecular modeling of the RSV immature hexamer indicates that the N-terminal domains of CA must expand relative to the murine leukaemia virus mature hexamer to accommodate the p10 contact; this expansion is strikingly similar to recent cryotomography results for immature human immunodeficiency virus particles.

Acute and long-term safety of external trigeminal nerve stimulation for drug-resistant epilepsy.

Trigeminal nerve stimulation (TNS) is a novel therapy for drug-resistant epilepsy. We report in detail the safety of external TNS (eTNS), focusing on acute and long-term heart rate and systolic and diastolic blood pressure in response to TNS from the pilot feasibility study. The data indicate that eTNS of the infraorbital and supraorbital branches of the trigeminal nerve is safe and well tolerated.

Plasma membrane phosphoinositide organization by protein electrostatics.

Phosphatidylinositol 4,5-bisphosphate (PIP2), which comprises only about 1% of the phospholipids in the cytoplasmic leaflet of the plasma membrane, is the source of three second messengers, activates many ion channels and enzymes, is involved in both endocytosis and exocytosis, anchors proteins to the membrane through several structured domains and has other roles. How can a single lipid in a fluid bilayer regulate so many distinct physiological processes? Spatial organization might be the key to this. Recent studies suggest that membrane proteins concentrate PIP2 and, in response to local increases in intracellular calcium concentration, release it to interact with other biologically important molecules.

Outcome of a workshop on applications of protein models in biomedical research.

We describe the proceedings and conclusions from the "Workshop on Applications of Protein Models in Biomedical Research" (the Workshop) that was held at the University of California, San Francisco on 11 and 12 July, 2008. At the Workshop, international scientists involved with structure modeling explored (i) how models are currently used in biomedical research, (ii) the requirements and challenges for different applications, and (iii) how the interaction between the computational and experimental research communities could be strengthened to advance the field.

Oncoprotein-specific molecular interaction maps (SigMaps) for cancer network analyses.

Tumor-specific elucidation of physical and functional oncoprotein interactions could improve tumorigenic mechanism characterization and therapeutic response prediction. Current interaction models and pathways, however, lack context specificity and are not oncoprotein specific. We introduce SigMaps as context-specific networks, comprising modulators, effectors and cognate binding-partners of a specific oncoprotein. SigMaps are reconstructed de novo by integrating diverse evidence sources-including protein structure, gene expression and mutational profiles-via the OncoSig machine learning framework. We first generated a KRAS-specific SigMap for lung adenocarcinoma, which recapitulated published KRAS biology, identified novel synthetic lethal proteins that were experimentally validated in three-dimensional spheroid models and established uncharacterized crosstalk with RAB/RHO. To show that OncoSig is generalizable, we first inferred SigMaps for the ten most mutated human oncoproteins and then for the full repertoire of 715 proteins in the COSMIC Cancer Gene Census. Taken together, these SigMaps show that the cell's regulatory and signaling architecture is highly tissue specific.

Education and Outreach in Physical Sciences in Oncology.

Physical sciences are often overlooked in the field of cancer research. The Physical Sciences in Oncology Initiative was launched to integrate physics, mathematics, chemistry, and engineering with cancer research and clinical oncology through education, outreach, and collaboration. Here, we provide a framework for education and outreach in emerging transdisciplinary fields.

High-throughput computational structure-based characterization of protein families: START domains and implications for structural genomics.

SkyLine, a high-throughput homology modeling pipeline tool, detects and models true sequence homologs to a given protein structure. Structures and models are stored in SkyBase with links to computational function annotation, as calculated by MarkUs. The SkyLine/SkyBase/MarkUs technology represents a novel structure-based approach that is more objective and versatile than other protein classification resources. This structure-centric strategy provides a multi-dimensional organization and coverage of protein space at the levels of family, function, and genome. The concept of "modelability", the ability to model sequences on related structures, provides a reliable criterion for membership in a protein family ("leverage") and underlies the unique success of this approach. The overall procedure is illustrated by its application to START domains, which comprise a Biomedical Theme for the Northeast Structural Genomics Consortium as part of the Protein Structure Initiative. START domains are typically involved in the non-vesicular transport of lipids. While 19 experimentally determined structures are available, the family, whose evolutionary hierarchy is not well determined, is highly sequence diverse, and the ligand-binding potential of many family members is unknown. The SkyLine/SkyBase/MarkUs approach provides significant insights and predicts: (1) many more family members (approximately 4,000) than any other resource; (2) the function for a large number of unannotated proteins; (3) instances of START domains in genomes from which they were thought to be absent; and (4) the existence of two types of novel proteins, those containing dual START domain and those containing N-terminal START domains.

To B or not to B: PIP2 answers the question.

In a recent issue of Molecular Cell, Papayannopoulos et al. (2005) show that N-WASP activation of actin-related protein2/3 (Arp2/3) is ultrasensitive to PI(4,5)P(2) concentration. We discuss how interactions between basic regions in proteins and negatively charged membranes can produce sharp on-off switches that may regulate biological activity.